Machine-Learning-Based Consumption Estimation of Prestressed Steel for Prestressed Concrete Bridge Construction

نویسندگان

چکیده

Accurate prediction of the prestressed steel amount is essential for a concrete-road bridge’s successful design, construction, and long-term performance. Predicting required can help optimize design construction process, also project managers engineers estimate overall cost more accurately. The model was developed using data from 74 constructed bridges along Serbia’s Corridor X. study examined operationally applicable models that do not require indepth modeling expertise to be used in practice. Neural networks (NN) based on regression trees (RT) genetic programming (GP) were analyzed. In this work, first time, method multicriteria compromise ranking applied find optimal concrete bridges. optival GP determined VIKOR optimization; accuracy which expressed through MAPE criterion 9.16%. A significant average share 46.11% costs related steelworks, relation total costs, indicates paper implicit estimation costs.

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ژورنال

عنوان ژورنال: Buildings

سال: 2023

ISSN: ['2075-5309']

DOI: https://doi.org/10.3390/buildings13051187